




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					ArgumentParser(
         description=(
             "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
         )     )

     # Optional arguments for the launch helper
     parser.add_argument("--num_cores"  ,							type=lowerCAmelCase_  ,							default=1  ,							help="Number of TPU cores to use (1 or 8)."     )

     # positional
     parser.add_argument(
         "training_script"  ,							type=lowerCAmelCase_  ,							help=(
             "The full path to the single TPU training "
             "program/script to be launched in parallel, "
             "followed by all the arguments for the "
             "training script"
         )  ,							)

     # rest from the training program
     parser.add_argument("training_script_args"  ,							nargs=lowerCAmelCase_     )

     return parser.parse_args()



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					parse_args()

     # Import training_script as a module.
     lowerCAmelCase__            =					Path(args.training_script     )
     sys.path.append(str(script_fpath.parent.resolve()     )     )
     lowerCAmelCase__            =					script_fpath.stem
     lowerCAmelCase__            =					importlib.import_module(lowerCAmelCase_     )

     # Patch sys.argv
     lowerCAmelCase__            =					[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores     )]

     xmp.spawn(mod._mp_fn  ,							args=()  ,							nprocs=args.num_cores     )


if __name__ == "__main__":
     main()




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




